A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Application of Compressed Sensing on Crowdsensing-Based Indirect Bridge Condition Monitoring
Bridges are a vital component of the public transit system. However, as such infrastructure systems age, they sustain various sorts of damage, decreasing their performance and service life dramatically. In this setting, effective and efficient bridge health monitoring is critical to lowering maintenance costs and extending the service life of existing bridges. Traditional monitoring techniques require sensors being installed on bridges, which is costly and time-consuming. This paper presents a novel crowdsensing-based methodology to monitor the health condition of bridges through a number of smartphones in moving vehicles, i.e., indirect monitoring. By collecting continuous data from the smartphone users and extracting features from the data while they cross the bridge, the damage can be identified through quantifying the difference of the distributions of the features. The continuous data collection and transmission with high sampling frequency pose a particular challenge to the participation of the public, because this could drain the smartphone battery and data plan quickly. In this paper, compressed sensing is introduced into this crowdsensing framework. The compressed sensing can recover the signal from much fewer samples than the ones required by Nyquist–Shannon sampling theorem through random sampling, which leads to more efficient data collection and transmission. Numerical analysis is conducted to validate the effectiveness of compressed sensing on indirect bridge condition monitoring.
Application of Compressed Sensing on Crowdsensing-Based Indirect Bridge Condition Monitoring
Bridges are a vital component of the public transit system. However, as such infrastructure systems age, they sustain various sorts of damage, decreasing their performance and service life dramatically. In this setting, effective and efficient bridge health monitoring is critical to lowering maintenance costs and extending the service life of existing bridges. Traditional monitoring techniques require sensors being installed on bridges, which is costly and time-consuming. This paper presents a novel crowdsensing-based methodology to monitor the health condition of bridges through a number of smartphones in moving vehicles, i.e., indirect monitoring. By collecting continuous data from the smartphone users and extracting features from the data while they cross the bridge, the damage can be identified through quantifying the difference of the distributions of the features. The continuous data collection and transmission with high sampling frequency pose a particular challenge to the participation of the public, because this could drain the smartphone battery and data plan quickly. In this paper, compressed sensing is introduced into this crowdsensing framework. The compressed sensing can recover the signal from much fewer samples than the ones required by Nyquist–Shannon sampling theorem through random sampling, which leads to more efficient data collection and transmission. Numerical analysis is conducted to validate the effectiveness of compressed sensing on indirect bridge condition monitoring.
Application of Compressed Sensing on Crowdsensing-Based Indirect Bridge Condition Monitoring
Lecture Notes in Civil Engineering
Gupta, Rishi (editor) / Sun, Min (editor) / Brzev, Svetlana (editor) / Alam, M. Shahria (editor) / Ng, Kelvin Tsun Wai (editor) / Li, Jianbing (editor) / El Damatty, Ashraf (editor) / Lim, Clark (editor) / Mei, Qipei (author)
Canadian Society of Civil Engineering Annual Conference ; 2022 ; Whistler, BC, BC, Canada
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022 ; Chapter: 9 ; 123-132
2023-08-17
10 pages
Article/Chapter (Book)
Electronic Resource
English
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